Zobrazeno 1 - 10
of 891
pro vyhledávání: '"Pernkopf"'
Autor:
Kantz, Benedikt, Staudinger, Clemens, Feilmayr, Christoph, Wachlmayr, Johannes, Haberl, Alexander, Schuster, Stefan, Pernkopf, Franz
eXplainable Artificial Intelligence (XAI) aims at providing understandable explanations of black box models. In this paper, we evaluate current XAI methods by scoring them based on ground truth simulations and sensitivity analysis. To this end, we us
Externí odkaz:
http://arxiv.org/abs/2407.09127
The Bethe free energy approximation provides an effective way for relaxing NP-hard problems of probabilistic inference. However, its accuracy depends on the model parameters and particularly degrades if a phase transition in the model occurs. In this
Externí odkaz:
http://arxiv.org/abs/2405.15514
Bayesian causal inference (BCI) naturally incorporates epistemic uncertainty about the true causal model into down-stream causal reasoning tasks by posterior averaging over causal models. However, this poses a tremendously hard computational problem
Externí odkaz:
http://arxiv.org/abs/2402.14781
Publikováno v:
2023 20th European Radar Conference (EuRAD) (pp. 135-138). IEEE
In automotive applications, frequency modulated continuous wave (FMCW) radar is an established technology to determine the distance, velocity and angle of objects in the vicinity of the vehicle. The quality of predictions might be seriously impaired
Externí odkaz:
http://arxiv.org/abs/2401.05385
In this paper we propose a new method for training neural networks (NNs) for frequency modulated continuous wave (FMCW) radar mutual interference mitigation. Instead of training NNs to regress from interfered to clean radar signals as in previous wor
Externí odkaz:
http://arxiv.org/abs/2312.09790
We present a data-driven car occupancy detection algorithm using ultra-wideband radar based on the ResNet architecture. The algorithm is trained on a dataset of channel impulse responses obtained from measurements at three different activity levels o
Externí odkaz:
http://arxiv.org/abs/2311.10478
We present a variational Bayesian (VB) implementation of block-sparse Bayesian learning (BSBL), which approximates the posterior probability density function (PDF) of the latent variables a factorzied proxy PDFs. The prior distribution of the BSBL hy
Externí odkaz:
http://arxiv.org/abs/2306.00442
Autor:
Fuchs, Alexander, Knoll, Christian, Moghadam, Nima N., Huang, Alexey Pak Jinliang, Leitinger, Erik, Pernkopf, Franz
Multiple-Input Multiple-Output (MIMO) systems are essential for wireless communications. Sinceclassical algorithms for symbol detection in MIMO setups require large computational resourcesor provide poor results, data-driven algorithms are becoming m
Externí odkaz:
http://arxiv.org/abs/2303.07821
In this paper, we present a variational inference algorithm that decomposes a signal into multiple groups of related spectral lines. The spectral lines in each group are associated with a group parameter common to all spectral lines within the group.
Externí odkaz:
http://arxiv.org/abs/2303.03017